Confidently Wrong: Calibration Risks in LLMs for African Languages

Evaluates four open-weight LLMs on African-language benchmarks and tests per-language temperature scaling to reduce overconfidence and improve calibration without retraining.

Endorsed byCClaude

Evaluates four open-weight LLMs on African-language benchmarks and tests per-language temperature scaling to reduce overconfidence and improve calibration without retraining.

Endorsed byCClaude

Project Details

Large language models are increasingly used in African languages, but little is known about whether their confidence remains trustworthy. We evaluate four open-weight LLMs across African-language truthfulness and knowledge benchmarks and find a consistent safety risk: models become less accurate while remaining highly confident, leading to systematic overconfidence and substantially worse calibration than in English. This increases the likelihood that users will trust incorrect information in high-stakes settings such as health, education, and civic information. We show that simple per-language temperature scaling dramatically reduces calibration error without retraining, restoring confidence estimates to near-English levels. Our results highlight calibration as an overlooked African AI safety challenge and demonstrate a practical deployment-time intervention for reducing confidently wrong model outputs.

Hackathon paper: Confidently Wrong: Measuring and Mitigating Calibration Risks in LLMs for African Languages
Code: github.com/rexsimiloluwah/aims-team-apart-ai-safety-hackathon
Slides: Slides
Recording: Presentation recording

Theory of Impact

Imagine a patient asking an LLM about their symptoms in their native language. The model confidently recommends a treatment, but its advice is wrong. The patient, unaware of the model’s uncertainty, follows it and suffers harm.

This risk is particularly concerning for low-resource languages, where model reliability is often less thoroughly evaluated. In healthcare and other critical areas, a confident but incorrect answer can have serious consequences.

This research will identify calibration failures across models and languages and evaluate techniques that help models communicate uncertainty, abstain when appropriate, or defer to healthcare professionals. By releasing reproducible benchmarks, evaluation tools and research findings, we aim to help developers assess reliability beyond accuracy alone and build safer, more trustworthy multilingual AI systems.

People

Similoluwa Okunowo

Team Member

Track Record

Global South AIS Hackathon (Apart Research), 2026

Grants Received– no grants recorded

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Funding Asks

Confidently Wrong: Measuring and Mitigating Calibration Risks in LLMs for African Languages

Open

Minimum

$3,000

Ideal

$12,000

What this ask is

We will investigate calibration risks in large language models for low-resource languages, measuring whether model confidence reflects answer correctness and developing calibrated abstention techniques for safer deployment.

We will evaluate selected open-weight and closed-source models via APIs using accuracy, Expected Calibration Error (ECE), overconfidence and AUROC. We will test lightweight interventions, such as temperature scaling, and evaluate whether calibrated abstention helps models recognise when they should withhold an answer or defer to a human.

Deliverables will include a reproducible evaluation pipeline, benchmark results, a qualitative analysis of confidently incorrect responses, and an open research report with practical recommendations for multilingual AI safety and trustworthy AI in low-resource contexts.

How the money will be spent

For the minimum budget of $3,000

LLM API costs: $900
Modal GPU compute: $1,000
Data preparation and annotation: $800
Storage and other infrastructure: $100
Other operational costs: $200

Total: $3,000

For the ideal budget of $12,000

LLM API costs: $4,000
Modal GPU compute: $4,000
Data preparation and annotation: $2,000
Storage and other infrastructure: $1,000
Other operational costs: $1,000

Total: $12,000

Discussion

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Private UserPrivate7d?

CClaude FormanekJudge · Global South AIS HackathonEndorsement7d

Strengths

This is a strong paper that identifies a specific failure mode with broad implications. The work shows that models become systematically overconfident as they move from English to African languages, so they are less accurate yet more confident at the same time. That is the worst combination for trust, and it applies to any high-stakes African-language deployment rather than a single use case, which gives the finding a high impact ceiling.
The work also goes beyond diagnosis. The team proposes three interventions and tests their efficacy rather than just suggesting them.
Execution is solid and well-evidenced. The scoring choice is well-justified, and the central finding is backed by bootstrap confidence intervals, a held-out calibration split, and multiple metrics. The presentation is excellent.

Weaknesses

The main limitation is interpretive. On AfriMMLU the African-language accuracy sits near the chance floor (about 0.29 against 0.25), so some of the measured overconfidence reflects a model with almost no signal rather than a model that is confidently wrong about things it could know. The two stories have different implications and the paper does not fully separate them. The best-performing fix also carries a deployment tension. Per-language temperature scaling needs labelled in-language calibration data, which is exactly the scarce resource in these languages.

Recommendations for the authors

A few targeted additions would round this out into a full paper. First, move the evaluation closer to frontier models to see if the finding holds. The near-chance accuracy is on small open-weight models, so a stronger model would likely clear the chance floor and provide the signal needed to cleanly separate "confidently wrong" from "essentially guessing", while also testing whether the overconfidence generalises beyond open-weight models. If frontier models remain near chance on these languages, that is itself a striking result worth reporting. Second, quantify how little calibration data the temperature-scaling fix needs with a simple sensitivity curve over split size, which would directly support the "deployable now" argument and address the labelled-data tension.

DDr. Samuel SegunJudge · Global South AIS Hackathon7d

The project is well-executed and addresses an important problem. However, there are two notable limitations:

The evaluation is restricted to four open-weight models on two benchmarks (Uhura-TruthfulQA and AfriMMLU), with frontier closed models and reasoning models explicitly out of scope. Because calibration behavior may differ substantially across model families and scales, the generality of the "confidently wrong" finding — and the transferability of the temperature-scaling remedy — cannot be confirmed for the systems most likely to be deployed in high-stakes African-language contexts.
Accuracy on these benchmarks sits near chance for most African languages, which the authors acknowledge bounds what can be claimed. This creates an interpretive difficulty: when a model scores ~29% on a four-option knowledge task, the reliability of the "confidence" signal being calibrated is itself in question. The paper documents systematic overconfidence, but the practical severity of the harm — confident wrong answers on questions users actually ask in deployment — is not directly measured, and the low ceiling of the benchmarks limits how much the ECE numbers can tell us about real-world risk magnitude.